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Branch Retinal Artery Occlusion (BRAO) has become a serious disorder causing permanent loss of vision. It is mainly caused due to the rupture of blood vessels which is too small in size and thereby increasing the risk for efficient detection. Therefore an efficient detection scheme is necessary to evaluate appropriately and pave the way for systemic therapy to preserve or recover vision in the affected eye. In this paper, a fully automatic detection scheme has been developed for accurate segmentation followed by classification of BRAO volumes using the fundus images. Coming to the point this is the first automatic classification framework using an optimized neural network for classifying BRAO. It's mainly a three-step process where the first step concentrates on removing noise and thereby mproving the quality of fundus image. According to which an boosted anisotropic diffusionbased enhancement filter is used here, which effectively removes the noise. This is justified using image quality metrics. Second step here involves adaptive cluster with super pixel segmentation which clearly segments the affected area with high contrast. For the segmented image IR Analyzer is used for extracting region properties. In the final step gray wolf optimization-based convolution neural network (GWO-CNN) classifier is used. Here GWO-CNN classifier is used to differentiate the normal and abnormal images. The effectiveness of the above method is evaluated and obtained an accuracy of 98.57% when it is implemented in MATLAB 2018 software. [ABSTRACT FROM AUTHOR]

High rated and active research in Ophthalmic field has paved the way for the detection of several retinal disorders which helps the ophthalmologist, in scheduling and executing timely treatment. There exist different imaging device for retinal examination in ophthalmology with varying degrees of resolution. The images obtained from such imaging devices contains noise which must be eliminated for producing accurate results. This paper mainly concentrates on using boosted anisotropic diffusion filter (BADF) which outperforms the existing anisotropic diffusion filter and is compared with different filters to authenticate the performance. Various performance measures are taken into considerations which are mean square error, structural similarity index, peak signal to noise ratio and signal to noise ratio. The proposed work is tested for 20 images (10 abnormal BRAO from clinical database and 10 normal images). This paper clearly concludes that BADF helps the radiologist in accurate retinal examination and thereby reducing the risk factors. [ABSTRACT FROM AUTHOR]

This paper proposes an intellectual classification system to classify normal and abnormal CT scan lung images. Prediction of cancer cells from lung image is such a difficult task for physicians and researchers. A novel technique for lung tumour detection with Grey Wolf Optimized & Whale Optimization Algorithm – Support Vector Machine (GWO & WOA-SVM) is proposed. CT scan images are used as input images. Investigations focused on the segmentation and classification part to find the lung lesion region. For finding the lung abnormality and lesion region in the lung, four different stages are used. The first stage is the image acquisition process. The second stage is the image pre-processing and enhancement stage, here Advanced Clustering (AC) technique is used. The next stage is the segmentation process, here the advanced surface normal overlap (ASNO) lung segmentation algorithm is used. Finally, the lung lesion is classified using the hybrid classifier followed by a different optimization technique. The research gap for this paper is to find the lung lesion using an optimization-based hybrid classification technique. Nowadays, classification decision and treatment of lung tumours are based on symptoms and radiological appearance. Here Hybrid Classifier (KNN-SVM) is used to classify 700 images; it is observed from the results that the Hybrid classifier KNN-SVM demonstrated the highest classification accuracy rate of 97.6% among other methods. [ABSTRACT FROM AUTHOR]

Medical image detection and classification play an important role in medical research. Tumour detection is a process of crucial importance in oncology. The aim of this paper is to devise an approach for efficient image enhancement, tumour detection, and classification on computed tomographic lung images. Three different application-related methods on computed tomography (CT) images have been designed and implemented in this work. The first method, which was based on image quality, enhances the performance of the medical images. Generally, the CT images are very sensitive to noise and are difficult to handle. Proper care may be taken by introducing some preprocessing algorithms like enhancement algorithms and filters. According to this, an image enhancement algorithm, Wavelet Shrinkage adaptive histogram Equalization (WSAHE), with anisotropic diffusion filter (ADF) was introduced to improve the contrast of the CT images and denoising. In the second method Seed Region growing with Random walk segmentation algorithm (SRGWRWS) is proposed for lung tumour segmentation. In the third method GLCM features, FOS features, structural features, and combinational features are calculated from the segmented lung images. After segmentation, the tumour classification is done by Multi-class support vector machine (MCSVM) and Descend RUSBOOST classifiers. Here, MCSVM classifier was used to find whether the input CT tumour image is suspicious, benign or malignant. Overall accuracy of 92% is obtained using Descend RUSBOOST classifier with GLCM features, FOS features, structural features, combinational features and overall accuracy of 97% is obtained using MCSVM classifier with GLCM feature in MATLAB 2017a software. [ABSTRACT FROM AUTHOR]